Papers
8
Total Citations
91
H-Index
5
About
Marwan Qaid Mohammed is a leading researcher in robotic manipulation, with a focus on enabling robots to intelligently interact with their environments through grasping, picking, and placing objects. His work spans deep reinforcement learning, hybrid robotic systems, and trajectory planning, addressing critical challenges in cluttered and occluded settings. Mohammed’s most cited paper, “Review of Learning-Based Robotic Manipulation in Cluttered Environments” (2022, 39 citations), provides a comprehensive overview of the field, while his foundational studies on deep reinforcement learning for pick-and-place tasks (2020, 17 citations) and grasping under clutter and occlusion (2021, 16 citations) have significantly advanced dexterous robotic capabilities. He has also contributed to dynamic modeling and control of hybrid manipulators for precision applications like laser contour machining, as well as innovative approaches such as color-matching-based grasping and self-supervised learning with minimal resources. With over 90 cumulative citations across his publications, Mohammed’s work is widely recognized for bridging theory and practical implementation, making him a notable figure in robotics research.
Research Focus
Key Achievements
Top Papers
- 1Review of Learning-Based Robotic Manipulation in Cluttered Environments39 citations · 2022
- 2Pick and Place Objects in a Cluttered Scene Using Deep Reinforcement Learning17 citations · 2020
- 3Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion16 citations · 2021
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- 6Color Matching Based Approach for Robotic Grasping4 citations · 2021
- 7Smooth Sub-Phases Based Trajectory Planning for Exoskeleton System2 citations · 2017
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